class_docstr: we test data loading, model instantiation, forecasting consistency, in particular
  (1) load a testing data
  (2) transform data
  (3) instantiate the model and train
  (4) forecast, and the forecasting result agrees with the reference
class_docstr_tok: ["we","test","data","loading",",","model","instantiation",",","forecasting","consistency",",","in","particular","(","1",")","load","a","testing","data","(","2",")","transform","data","(","3",")","instantiate","the","model","and","train","(","4",")","forecast",",","and","the","forecasting","result","agrees","with","the","reference"]
code: def __init__(self, *args, **kwargs):
      super().__init__(*args, **kwargs)
  
      self.max_forecast_steps = 2
      self.maxlags = 6
      self.i = 0
  
      df, md = SeattleTrail(
          rootdir=join(rootdir, "data", "multivariate", "seattle_trail")
      )[0]
      t = int(df[md["trainval"]].index[-1].to_pydatetime().timestamp())
      k = "BGT North of NE 70th Total"
      data = TimeSeries.from_pd(df)
      cleanup = TransformSequence(
          [
              TemporalResample(missing_value_policy="FFill"),
              LowerUpperClip(upper=300),
              MinMaxNormalize(),
          ]
      )
      cleanup.train(data)
      self.train_data, self.test_data = cleanup(data).bisect(t)
      self.train_data_uni, self.test_data_uni = [
          d.univariates[k].to_ts()
          for d in [self.train_data, self.test_data]
      ]
  
      self.model = RandomForestForecaster(
          RandomForestForecasterConfig(
              max_forecast_steps=self.max_forecast_steps,
              maxlags=self.maxlags,
              target_seq_index=self.i,
              prediction_stride=1,
              n_estimators=20,
              invert_forecast=False,
          )
      )
  
file_path: merlion/tests/forecast/test_baggingtrees.py
imports: import logging
  from os.path import abspath, dirname, join
  import sys
  import unittest
  from merlion.evaluate.forecast import ForecastMetric
  from merlion.models.forecast.trees import RandomForestForecaster, RandomForestForecasterConfig
  from merlion.transform.normalize import MinMaxNormalize
  from merlion.transform.sequence import TransformSequence
  from merlion.transform.resample import TemporalResample
  from merlion.transform.bound import LowerUpperClip
  from merlion.utils import TimeSeries
  from merlion.models.utils.rolling_window_dataset import RollingWindowDataset
  from ts_datasets.forecast import SeattleTrail
label_desc: Use image node_id 1 to create a new TestRandomForestForecaster object from inherited base classes: unittest with example: obj = TestRandomForestForecaster()